Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation.

Acute graft-versus-host disease (aGvHD) remains a major cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). We performed RNA analysis of 1408 candidate genes in bone marrow samples obtained from 167 patients undergoing HSCT. RNA expression data were used in a machine learning algorithm to predict the presence or absence of aGvHD using either random forest or extreme gradient boosting algorithms. Patients were randomly divided into training (2/3 of patients) and validation (1/3 of patients) sets. Using post-HSCT RNA data, the machine learning algorithm selected 92 genes for predicting aGvHD that appear to play a role in PI3/AKT, MAPK, and FOXO signaling, as well as microRNA. The algorithm selected 20 genes for predicting survival included genes involved in MAPK and chemokine signaling. Using pre-HSCT RNA data, the machine learning algorithm selected 400 genes and 700 genes predicting aGvHD and overall survival, but candidate signaling pathways could not be specified in this analysis. These data show that NGS analyses of RNA expression using machine learning algorithms may be useful biomarkers of aGvHD and overall survival for patients undergoing HSCT, allowing for the identification of major signaling pathways associated with HSCT outcomes and helping to dissect the complex steps involved in the development of aGvHD. The analysis of pre-HSCT bone marrow samples may lead to pre-HSCT interventions including choice of remission induction regimens and modifications in patient health before HSCT.

LPS independent activation of the pro-inflammatory receptor Trem1 by C/EBPε in granulocytes.

C/EBPε is a critical transcriptional factor for granulocyte differentiation and function. Individuals with germline mutations of C/EBPε fail to develop normal granulocytes and suffer from repeated infections. In order to gain a global view of the transcriptional machinery regulated by C/EBPε, we performed whole-genome ChIP-Seq using mouse bone marrow cells. To complement the C/EBPε DNA binding analyses, RNA-Sequencing was done in parallel using sorted mature and immature granulocytes from WT and C/EBPε KO bone marrow. This approach led to the identification of several direct targets of C/EBPε, which are potential effectors of its role in granulocytic differentiation and function. Interestingly, Trem1, a gene critical to granulocyte function, was identified as a direct C/EBPε target gene. Trem1 expression overlaps very closely with expression signature of C/EBPε during hematopoietic development. Luciferase reporter and EMSA assays revealed that C/EBPε binds to the regulatory elements of Trem1 and regulates its expression during granulocytic differentiation. In addition, we provide evidence that inflammatory stimuli (LPS) can also control the expression of Trem1 independent of C/EBPε. Overall, this study provides comprehensive profiling of the transcriptional network controlled by C/EBPε during granulopoiesis and identifies Trem1 as one of its downstream effectors involved in eliciting an immune response.